Designing complex engineering systems such as self-driving cars, electronic and photonic integrated circuits, requires design automation software to complete many challenging tasks that are impossible or too time-consuming if done manually. In practice, almost all engineering designs are subject to such unavoidable uncertainties as noise, fabrication process variations and insufficient knowledge about external environments. These uncertainties often cause performance degradations, system failures and sometimes fatal accidents. However, existing design automation software requires massive data samples from time-consuming computer simulations or non-trivial measurement when uncertainties are involved. This project uses electronic and photonic integrated circuits as driving examples, and will develop novel design automation algorithms to improve the performance and reliability under various uncertainties. The education components of this project include creating two graduate courses of uncertainty and data analysis, training future workforce through undergraduate and graduate research. The outreach education and training through the awardee institution and through academic conferences will enable technology and knowledge transfer to a broad community. Although this project targets on applications in electronics and photonics, the developed algorithms and theory will be applicable to many other domains such as autonomous driving, renewable energy systems, and medical imaging. Since uncertainty-aware photonic design automation is still at its early stage, this project will enable a new field of important research. The resulting algorithms and tools will support the foreseeable large-scale photonic integration which will boost the performance of future computing and communication systems.
The technical goal of this project is to develop novel uncertainty-aware electronic and photonic design automation algorithms that require only a small data set and a very low computational cost in the design flow. This project will span three research topics: uncertainty-aware simulation, optimization and data-driven variation modeling. Firstly, novel algorithms will be developed to address several long-standing challenges in the forward uncertainty quantification of electronic and photonic circuits, such as the coupled impact of fundamentally different types of uncertainties and long-term probabilistic simulation errors. Secondly, leveraging the developed forward uncertainty simulator, this project will further develop ultra-fast optimization tools to improve the yield of electronic and photonic circuits. The main focus will be investigating large-scale "non-sampling" stochastic optimization algorithms. The developed algorithms will enable rigorous yield optimization with "small" simulation data sets and thus significantly reduce the software runtime on a computer. Finally, rigorous statistical estimation algorithms will be developed to calibrate critical device model parameters and to extract statistical variability distributions based on limited and noisy indirect circuit-level measurement data. The designed algorithms and prototyping software will be validated by practical design cases.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
